Triple
T4298767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Earle Hagen |
E99779
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Hagen
Hagen is a surname of German origin borne by various notable individuals across fields such as music, sports, and academia.
|
E571843
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hagen | Statement: [Earle Hagen, familyName, Hagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hagen Context triple: [Earle Hagen, familyName, Hagen]
-
A.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
B.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
C.
Recklinghausen
Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
-
D.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
E.
Hennef
Hennef is a town in North Rhine-Westphalia, Germany, situated on the river Sieg near Bonn and known for its mix of residential areas, industry, and surrounding countryside.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hagen Triple: [Earle Hagen, familyName, Hagen]
Generated description
Hagen is a surname of German origin borne by various notable individuals across fields such as music, sports, and academia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hagen Target entity description: Hagen is a surname of German origin borne by various notable individuals across fields such as music, sports, and academia.
-
A.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
B.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
-
C.
Recklinghausen
Recklinghausen is a city in the Ruhr area of North Rhine-Westphalia, western Germany, known historically for coal mining and its role as a regional administrative center.
-
D.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
E.
Hennef
Hennef is a town in North Rhine-Westphalia, Germany, situated on the river Sieg near Bonn and known for its mix of residential areas, industry, and surrounding countryside.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3509d39348190aa83304661230cba |
completed | March 12, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1351018b48190910c3dad6c0f9850 |
completed | March 23, 2026, 12:41 p.m. |
| NEDg | Description generation | batch_69c1381233b081908af8982dce6a1db1 |
completed | March 23, 2026, 12:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1387a38788190b8cf2b04e5eeb5e3 |
completed | March 23, 2026, 12:56 p.m. |
Created at: March 12, 2026, 11:08 p.m.